Easily implementable time series forecasting techniques for resource provisioning in cloud computing

Michel Fliess, Cédric Join (), Maria Bekcheva, Alireza Moradi () and Hugues Mounier ()
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Michel Fliess: LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques
Cédric Join: CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques
Maria Bekcheva: Inagral, L2S - Laboratoire des signaux et systèmes - UP11 - Université Paris-Sud - Paris 11 - CentraleSupélec - CNRS - Centre National de la Recherche Scientifique
Alireza Moradi: Inagral
Hugues Mounier: L2S - Laboratoire des signaux et systèmes - UP11 - Université Paris-Sud - Paris 11 - CentraleSupélec - CNRS - Centre National de la Recherche Scientifique

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Abstract: Workload predictions in cloud computing is obviously an important topic. Most of the existing publications employ various time series techniques, that might be difficult to implement. We suggest here another route, which has already been successfully used in financial engineering and photovoltaic energy. No mathematical modeling and machine learning procedures are needed. Our computer simulations via realistic data, which are quite convincing, show that a setting mixing algebraic estimation techniques and the daily seasonality behaves much better. An application to the computing resource allocation, via virtual machines, is sketched out.

Keywords: Cloud computing; computing resources; virtual machines; forecasting; time series; nonstandard analysis; trend; quick fluctuation; machine learning; estimation; seasonality (search for similar items in EconPapers)
Date: 2019-04-23
New Economics Papers: this item is included in nep-cmp
Note: View the original document on HAL open archive server: https://polytechnique.hal.science/hal-02024835v3
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Published in 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019, Apr 2019, Paris, France. ⟨10.1109/codit.2019.8820396⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02024835

DOI: 10.1109/codit.2019.8820396

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